Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

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Jun 12, 2015
Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi
Figure 1 for Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

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We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining a reasonable computational cost.

* European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.291-296, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)  
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